Neural Network Prediction Research Articles

A novel data-driven framework for enhancing the consistency of deposition contours and mechanical properties in metal additive manufacturing

The accuracy and quality of part formation are crucial considerations. However, the laser directed energy deposition (L-DED) process often leads to irregular changes in deposition contours and mechanical properties across parts due to complex flow fields and temperature variations. Hence, to ensure the forming accuracy and quality, it is necessary to achieve precise monitoring and appropriate parameter adjustments during the processing. In this study, a machine vision method for real-time monitoring is proposed, which combines target tracking and image processing techniques to achieve accurate recognition of deposition contours under noisy conditions. Through comparative verification, the measurement accuracy reaches as high as 98.98 %. Leveraging the monitoring information, a bidirectional prediction neural network is proposed to accomplish layer-by-layer forward prediction of layer height. Meanwhile, inverse prediction is employed to determine the processing parameters required for achieving the desired layer height, facilitating the optimization of the deposition contours. It was found that as the processing parameters were adjusted layer-by-layer to achieve consistent deposition contours, there was also a tendency towards consistent changes in microstructure and mechanical properties. The standard deviations of primary dendrite arm spacing (PDAS) and ultimate tensile strength (UTS) at different positions decrease by over 52.2 % and 61.4 %, respectively. This study reveals the consistent patterns of variation in deposition contours and mechanical properties under data-driven variable parameter processing, laying an important foundation for future exploration of the complex process-structure-performance (PSP) relationship in L-DED.

The Propagation and Reduction of Uncertainty Left Unquantified by Confidence Intervals, P-values, Neural Network Predictions, Posterior Distributions, and Other Statistical Results

Abstract In the use of statistical models to analyze data, there is not only the uncertainty quantified by the models but also uncertainty about which models are adequate for some purpose, such as weighing the evidence for or against a hypothesis of scientific interest. This paper provides methods for propagating such unquantified uncertainty to the results under a unified framework of adequate model averaging. Specifically, the weight of each model used in the average is the probability that it is the most useful model. To allow for the case that none of the models considered would be useful, a catch-all model is included in the model average at a different level of the hierarchy. The catch-all model is the vacuous model in imprecise probability theory, the model that puts no restrictions on the probabilities of statements about the unknown values of interest. That enables defining the proportion of the uncertainty left unquantified by a model as the probability that it is inadequate in the sense of being less useful than the catch-all model. A lower bound for the proportion of unquantified uncertainty of the averaged model decreases as more models are added to the average.

Artificial neural network prediction of postoperative complications in papillary thyroid microcarcinoma based on preoperative ultrasonographic features.

To predict post-thyroidectomy complications in papillary thyroid microcarcinoma (PTMC) patients using a deep learning model based on preoperative ultrasonographic features. This study addresses the global rise in PTMC incidence and the challenges in treatment decision-making with high-resolution ultrasonography. This study enrolled 1638 patients with clinically staged cN0 PTMC who received surgical treatment from 1997 to 2019 at Beijing Friendship Hospital. Deep learning model was developed using fully connected neural network. Feature selection included 1000 iterations of Bootstrap sampling and Recursive Feature Elimination (RFE) to identify the top 10 features. Data preprocessing involved normalization and imputation for missing values. SMOTE addressed class imbalance. The model was trained and tested on random data split, with performance metrics including Accuracy (ACC), Area Under the Curve (AUC), Sensitivity (SEN), and Specificity (SPE), visualized through a ROC curve and confusion matrix. The fully connected deep neural network model demonstrated high accuracy (ACC 0.81), Area Under the Curve (AUC 0.74), sensitivity (SEN 0.65), and specificity (SPE 0.83) and visualized by ROC curve and confusion matrix. These results highlight the model's reliability and potential as an effective tool in predicting postoperative complications and assisting in clinical decision-making for PTMC patients. This study highlights the potential of deep learning in enhancing medical predictions and personalized healthcare. Despite promising results, limitations include a single-center data source and unconsidered factors like lifestyle and genetics. Future research should expand data sources, include more influencing factors, and refine algorithms to improve accuracy and applicability in thyroid cancer treatment.

Prediction of Mechanical Properties of Lattice Structures: An Application of Artificial Neural Networks Algorithms.

The yield strength and Young's modulus of lattice structures are essential mechanical parameters that influence the utilization of materials in the aerospace and medical fields. Currently, accurately determining the Young's modulus and yield strength of lattice structures often requires conduction of a large number of experiments for prediction and validation purposes. To save time and effort to accurately predict the material yield strength and Young's modulus, based on the existing experimental data, finite element analysis is employed to expand the dataset. An artificial neural network algorithm is then used to establish a relationship model between the topology of the lattice structure and Young's modulus (the yield strength), which is analyzed and verified. The Gibson-Ashby model analysis indicates that different lattice structures can be classified into two main deformation forms. To obtain an artificial neural network model that can accurately predict different lattice structures and be deployed in the prediction of BCC-FCC lattice structures, the artificial network model is further optimized and validated. Concurrently, the topology of disparate lattice structures gives rise to a certain discrete form of their dominant deformation, which consequently affects the neural network prediction. In conclusion, the prediction of Young's modulus and yield strength of lattice structures using artificial neural networks is a feasible approach that can contribute to the development of lattice structures in the aerospace and medical fields.

Genetic regulation of B cell receptor signaling pathway: Insights from expression quantitative trait locus analysis using a mixed model

The B cell receptor (BCR) signaling pathway regulates non-immune cellular response through various pathways like MAPK, NF-kB, and PI3K-Akt. This study aimed to identify expression quantitative trait loci (eQTL) and their regulatory functions on BCR signaling pathway genes. A mixed model was employed to analyze eQTL using RNA expression levels in lymphoblastoid from 376 Europeans in the GEUVADIS dataset. In total, 266 SNPs, including 115 cis-acting SNPs, were found for association with transcription of 13 genes (P < 5 × 10−8), revealing 19 independent signals for five genes through linkage disequilibrium analysis. Functional analysis, aligning them with DNase sensitive sites, transcription factor binding sites, histone modification, promoters/enhancers, CpG islands, and ChIA-PET, identified regulatory variants targeting SYK, VAV2, and PLCG2. Notably, rs2562397 was validated as a SYK promoter variant, and rs694505, rs636667, and rs4889409 were confirmed as enhancer variants for VAV2 and PLCG2. Their allelic differences in gene expression were also confirmed using ENCODE ChIP-seq and Sei neural network prediction. Persistent differential expression of these genes by alleles might impact the adaptive immune system, vascular development, and/or relevant diseases that have been previously associated with other variants of the genes. Comprehensive genetic architecture studies of the BCR signaling pathway, along with experiments demonstrating related mechanisms, will greatly contribute to understanding the underlying mechanisms of relevant disease development and implementing precision medicine.

RETRACTED: Relationships among fall cone flow index and consistency identifiers of fine-grained soils with emphasis on toughness limit

It is well known that plasticity characteristics are one of the most important factors controlling the engineering behavior of fine-grained soils. In this regard, Atterberg limits are used for evaluation of plasticity and strength behavior of soils. Of all the plasticity identifiers, fall cone flow index, which is the slope of the flow curve is a descriptor of plasticity and undrained shear strength characteristics of cohesive soils. On the other hand, toughness limit is also efficient in prediction of many index properties of soils including plasticity characteristics, compression index as well as residual friction angle. This study is an attempt to establish relationships among fall cone flow index and consistency identifiers of fine-grained soils. Emphasis is given on toughness limit, which is believed to be a practical and efficient approach in assessment of fall cone flow index. Regression-based equations establishing relationships between fall cone flow index, plasticity index, plasticity ratio, toughness limit and liquid limit were presented. Besides, the efficiency of artificial neural networks in prediction of toughness limit and fall cone flow index was investigated. It should be noted that established relationships using regression equations are based on regional data, which come up with coefficient of determination (R2) values ranging between 0.706 and 0.978. On the other hand, ANN models were used to model global data, R2 values were obtained to be between 0.586-0.972 and 0.522-0.889 for training and testing phases, respectively. Relationships concerning local and global data were presented, along with analysis of effectiveness of application of relationships established for analysis of global data.

Genetic modification optimization technique: A neural network multi-objective energy management approach

In this study, a Neural Network-Enhanced Gene Modification Optimization Technique was introduced for multi-objective energy resource management. Addressing the need for sustainable energy solutions, this technique integrated neural network models as fitness functions, representing an advancement in artificial intelligence-driven optimization. Data collected in the European Union covered greenhouse gas emissions, energy consumption by sources, energy imports, and Levelized Cost of Energy. Since different configurations of energy consumption by sources lead to varying greenhouse gas emissions, costs, and imports, neural network prediction models were used to project the effect of new energy combinations on these variables. The projections were then fed into the gene modification optimization process to identify optimal configurations. Over 28 generations, simulations demonstrated a 46 percent reduction in energy costs and a 9 percent decrease in emissions. Human bias and subjectivity were mitigated by automating parameter settings, enhancing the objectivity of results. Benchmarking against traditional methods, such as Euclidean Distance, validated the superior performance of this approach. Furthermore, the technique's ability to visualize chromosomes and gene values offered clarity in optimization processes. These results suggest significant advancements in the energy sector and potential applications in other industries, contributing to the global effort to combat climate change.

An integrated convolutional neural network prediction framework for in situ shale oil content based on conventional logging data

The quantification of total organic carbon (TOC) and the free hydrocarbon content (S 1 ) is crucial for evaluating the shale oil generation and bearing properties of source rocks. This study aimed to enhance the accuracy of TOC and S 1 quantification in shale oil evaluations. The scope encompassed the development of a novel deep learning framework to overcome the limitations of traditional physical and machine learning or deep learning methods. This paper proposes an integrated swarm optimization algorithm–convolutional neural network/machine learning framework. This framework uses a swarm optimization algorithm for the hyperparameter optimization of a convolutional neural network or machine learning framework, utilizing experimental data from core samples preserved in liquid nitrogen alongside well logging data. The application of the proposed framework to the H11 well in the Subei Basin, China, using 110 core samples, demonstrated a superior performance. The results validate the framework's effectiveness in predicting the TOC and S 1 contents at various depths. The proposed framework stands out for its convenient methodology, wide application range and high precision in prediction. These attributes contribute significantly to the field of petroleum engineering and development, offering a novel approach that promises to enhance both the efficiency and accuracy of well logging evaluation.

Comparative predictive analysis using ANN and RCA for experimental investigation on branched and conventional micro heat pipe

The demand for compact, high-performance electronic devices rises, making thermal management in limited space crucial. This study presents a novel branched micro heat pipe (MHP) to solve this pressing problem. This novel method improves thermal performance for small-scale electronic cooling applications, surpassing conventional cooling methods. Innovative design has branched condenser of 12.7 mm evaporator, 24.6 mm adiabatic section and 12.7 mm condenser, each, compared to conventional MHP of identical size. Investigation encompasses a study of various parameters including working fluids, wettability characteristics, fill ratio, heat input, and cross-sectional geometries. The significant improvement in thermal performance by applying a hydrophobic surface treatment to the condenser section, increasing capillary force and condensate transport. Ammonia and de-ionized water excel thermal performance at lower and higher applied heat input (Qh), respectively. At higher Qh (>2 W), ammonia and toluene in the conventional MHP experience faster drying compared to the branched MHP, especially for lower fill ratio (0.6 FR). The branched MHP outperforms the conventional MHP, showing improved fluid circulation and performance, for fill ratios ranging from 0.65 to 0.4 and effectively transporting maximum heat flux in range of 10–14.4 W/cm2 with square mono-grooved MHP exhibits inferior performance compared to the trapezoidal and triangular mono-grooved. To provide a framework for future studies, we establish an artificial neural network (ANN) and statistical prediction model of thermal performance using the non-dimensional Kutateladze number (Ku), showcasing a robust correlation with experimental results and demonstrating only a minor deviation of ± 3 %.

Process Optimization for Antarctic Krill (Euphausia superba) Sauce Based on Back Propagation Neural Network Combined with Genetic Algorithm

Using Antarctic krill (Euphausia superba) as the research object, we optimized the process conditions for Antarctic krill sauce (AkS) by including three factors (salt addition, rock sugar addition, and the oil-to-material ratio) and sensory evaluation as response values. The data from the response surface were fed into the back propagation (BP) neural network training, generating a model mapping the process conditions and sensory scores, which were subsequently combined with the genetic algorithm (GA) for global optimization to determine the optimal process for AkS preparation. The results revealed that the response surface model was well suited to the BP neural network training and prediction sets, with correlation values of 0.98 and 0.95, respectively. The fitting prediction effect was obvious for the sensory scoring results of the product. The parameters obtained from the GA’s global optimization search accord with the analytical results of the response surface. The findings demonstrated that combining a BP neural network with a GA can enhance the AkS preparation technique. Under optimal processing conditions, AkS has a high sensory score and protein and carbohydrate contents, moderate fat content, minimal fat oxidation, and non-detectable pathogens, indicating that the AkS in this study was nutritious and safe to consume.

Physics-Informed Neural Networks for Prediction of a Flow-Induced Vibration Cylinder

Abstract Flow-induced vibration (FIV) is a common phenomenon in ocean engineering for subsea structures with circular-shaped cross sections. A large amount of computational resources or experimental efforts are required to predict or measure the complicated motions and flow field surrounding a circular cylinder undergoing vortex-induced vibration (VIV). Physics-informed neural networks (PINNs) are powerful deep learning techniques for solving governing partial differential equations (PDEs) of dynamic systems as an alternative to complex numerical methods. In the present study, a framework is built employing PINNs for solving the Navier–Stokes equations to predict flows past an FIV cylinder using sparsely distributed spatiotemporal data inside the domain. The training process involves minimizing the supervised loss of flow data at these sparse points and the residuals of the governing PDEs. For the training of the PINN model, a moving frame around an FIV cylinder is used to collect the training flow data from two-dimensional direct numerical simulation results at a low Reynolds number. The structural displacements of the cylinder are also implemented in the residuals of the equations of the developed PINN. The performance of the PINN is evaluated by comparing the predicted contours of the surrounding flow velocities with the training data. The hydrodynamic forces prediction is achieved using the PINN-obtained flow field predictions combined with the force partitioning method.

Neural network prediction of thermal field spatiotemporal evolution during additive manufacturing: an overview

Neural network prediction of thermal field spatiotemporal evolution during additive manufacturing: an overview

Construction of a full color gamut color mixture model and a color prediction neural network model for rotor spinning melange yarn

This research is devoted to solving the bottleneck problem of spinning and color prediction of rotor spun melange yarn within the full color gamut range. Firstly, a full color gamut grid color mixture model with hue regulation range of 0–360°, saturation regulation range of 0-1 and lightness regulation range of 0-1 was constructed based on the preferred seven primary-color fibers through grid color mixing. Secondly, in the full color gamut grid color mixture model, 55 representative grid points were planned and corresponding melange yarns and fabrics were prepared based on grid point parameters, which served as the training set, testing set, and validation set for constructing neural network prediction models. Thirdly, based on the demand for color prediction of melange yarns, a color prediction model and a blending ratio prediction model with six sub-models are constructed by taking the reflectivity of the melange yarn fabric and the blending ratio of the primary-color fibers as input and output values. Finally, six prediction samples were produced to verify the predictive performance of the neural network prediction model. The results show that the average color difference between the predicted and measured colors is 1.06, and the average root mean square error value between the predicted and actual blending ratios is 0.48%. It indicates that the neural network prediction model based on the grid mixing of seven primary-color fibers can adapt to the prediction of the mixing color and blending ratio of primary-color fibers in the full color gamut. Due to the construction of six sub-models based on local hue regions, the prediction accuracy of the neural network prediction model has been greatly improved.

Propagating Transparency: A Deep Dive into the Interpretability of Neural Networks

In the rapidly evolving landscape of deep learning (DL), understanding the inner workings of neural networks remains a significant challenge. This need for transparency and accountability from DL models assumes particular importance as DL models become increasingly prevalent in decision-making processes. Interpreting these models is key to addressing this challenge. This paper offers a comprehensive overview of interpretable deep learning methods. It emphasizes gradient-based propagation techniques that shed light on the complex mechanisms driving neural network predictions. Through a systematic review, we categorize gradient-based interpretability approaches, delve into the theory of notable methods, and compare their strengths and weaknesses. Furthermore, we investigate various evaluation metrics for interpretable systems, often generalized under the term eXplainable Artificial Intelligence (XAI). We highlight their significance in assessing the faithfulness, robustness, localization, complexity, randomization, and adherence to the axiomatic principles of XAI methods. We aim to help researchers and practitioners work towards a more transparent future for artificial intelligence by providing an overview of the most recent developments in the field.

A Hybrid Localization Algorithm for Underwater Nodes Based on Neural Network and Mobility Prediction

A Hybrid Localization Algorithm for Underwater Nodes Based on Neural Network and Mobility Prediction

Optimizing stock market volatility predictions based on the SMVF-ANP approach

The stock market is considered one of the most complicated financial systems, comprising several components or inventories, whose prices vary substantially over time. Bursaries include revealing market tendencies over time. All investors in the stock market aim to maximize profits and reduce the associated risks. Stock currency forecasts are a significant financial concern that is being handled even more closely. In recent years, various neural network and hybrid models have surpassed classic linear and non-linear techniques to produce reliable prediction outcomes. This study investigates the efficacy of dynamic and effective stock market forecasting using neural network models. This study examines market transmission mechanisms and assesses the predicted links between multiple financial and economic factors. A stock market volatility and artificial network prediction (SMVF–ANP) approach is presented. The models analyzed included a multi-layer perceptron (MLP), dynamic artificial neural network, and generalized autoregressive conditional heteroscedasticity to extract additional input variables. The results reveal that the trade strategies led by the classification models yield superior risk-adjusted returns compared to the buy-and-hold approach and those led by the neural network and linear regression model-level estimate predictions. The numerical results show that the proposed SMVF-ANP method achieved a high performance ratio of 94.1%, an enhanced prediction ratio of 98.4%, a high stock market volatility rate of 96.7%, a reduced mean square percentage error ratio of 16.3%, a probability rate of 32.7%, an increased efficiency rate of 96.9%, and an accuracy ratio of 97.2% compared to other methods.

Determination of soluble solids content in tomatoes with different nitrogen levels based on hyperspectral imaging technique.

Tomato is sweet and sour with high nutritional value, and soluble solids content (SSC) is an important indicator of tomato flavor. Due to the different mechanisms of nitrogen uptake and assimilation in plants, exogenous supply of different forms of nitrogen will have different effects on the growth, development, and physiological metabolic processes of tomato, thus affecting the tomato flavor. In this paper, hyperspectral imaging (HSI) technique combined with neural network prediction model was used to predict SSC of tomato under different nitrogen treatments. Competitive adaptive reweighed sampling (CARS) and iterative retained information variable (IRIV) were used to extract the feature wavelengths. Based on the characteristic wavelength, the prediction models of tomato SSC are established by custom convolutional neural network (CNN) model that was constructed and optimized. The results showed that the SSC of tomato was negatively correlated with nitrogen fertilizer concentration. For tomatoes treated with different nitrogen concentrations, the residual predictive deviation (RPD) of CARS-CNN and IRIV-parallel convolutional neural networks (PCNN) reached 1.64 and 1.66, both more than 1.6, indicating good model prediction. This study provides technical support for future online nondestructive testing of tomato quality. PRACTICAL APPLICATION: The CARS-CNN and IRIV-PCNN were the best data processing model. Four customized convolutional neural networks were used for predictive modeling. The CNN model provides more accurate results than conventional methods.

Exchangeable acidity characteristics of farmland black soil in northeast China

Exchangeable aluminum (Exc-Al), an often overlooked yet indispensable soil parameter, predominantly contributes to soil exchangeable acidity. In this study, we utilized data from 53 sets of surface and subsurface black soil characteristics, including Exc-Al, exchangeable acid (Exc-acid), pH, soil organic matter (SOM), and available and total nutrient levels, to develop a neural network prediction model for estimating Exc-Al and Exc-acid in the black soil area of northeast China. The deterministic neural network model (NNM) was employed to predict Exc-Al and Exc-acid contents in 690 sets of surface and subsurface farmland soil samples with unknown Exc-Al and Exc-acid values. Subsequently, a black soil exchangeable acidity map for northeast China was generated through spatial interpolation. Our results revealed that the average Exc-Al and Exc-acid contents in the 53 surface soils were 0.82 and 0.93 cmol kg−1, respectively, while those in the corresponding subsurface soils were 0.58 and 0.70 cmol kg−1, respectively. Multi-layer perceptron (MLP) neural networks effectively simulated Exc-Al and Exc-acid contents in the surface and subsurface black soils, with calibrated determination coefficients (Radj2) of 0.95–0.96, relative root mean square errors (rRMSE) of 17.3%–24.8%, and statistical significance α at 0.001. The MLP estimations and spatial interpolations revealed that 2.0% and 17.6% of the surface black soil area, and 0% and 3.7% of the subsurface soil area exhibited Exc-Al content exceeding 2.0 and 1.0 cmol kg−1, respectively. Furthermore, 6.7% and 24.9% of the surface black soil area, and 0% and 6.3% of the subsurface soil area showed Exc-acid content exceeding 2.0 and 1.0 cmol kg−1, respectively. These findings break the limitation of relying solely on soil pH as the unique indicator, enrich our knowledge of black soil exchangeable acidity, and enhance our understanding of the black soil acidity status in northeast China.

A machine learning-based pipeline for multi-organ/tissue patient-specific radiation dosimetry in CT.

To develop a machine learning-based pipeline for multi-organ/tissue personalized radiation dosimetry in CT. For the study, 95 chest CT scans and 85 abdominal CT scans were collected retrospectively. For each CT scan, a personalized Monte Carlo (MC) simulation was carried out. The produced 3D dose distributions and the respective CT examinations were utilized for the development of organ/tissue-specific dose prediction deep neural networks (DNNs). A pipeline that integrates a robust open-source organ segmentation tool with the dose prediction DNNs was developed for the automatic estimation of radiation doses for 30 organs/tissues including sub-volumes of the heart and lungs. The accuracy and time efficiency of the presented methodology was assessed. Statistical analysis (t-tests) was conducted to determine if the differences between the ground truth organ/tissue radiation dose estimates and the respective dose predictions were significant. The lowest median percentage differences between MC-derived organ/tissue doses and DNN dose predictions were observed for the lung vessels (4.3%), small bowel (4.7%), pulmonary artery (4.7%), and colon (5.2%), while the highest differences were observed for the right lung's upper lobe (13.3%), spleen (13.1%), pancreas (12.1%), and stomach (11.6%). Statistical analysis showed that the differences were not significant (p-value > 0.18). Furthermore, the mean inference time, regarding the validation cohort, of the developed methodology was 77.0 ± 11.0 s. The proposed workflow enables fast and accurate organ/tissue radiation dose estimations. The developed algorithms and dose prediction DNNs are publicly available ( https://github.com/eltzanis/multi-structure-CT-dosimetry ). The accuracy and time efficiency of the developed pipeline compose a useful tool for personalized dosimetry in CT. By adopting the proposed workflow, institutions can utilize an automated pipeline for patient-specific dosimetry in CT. Personalized dosimetry is ideal, but is time-consuming. The proposed pipeline composes a tool for facilitating patient-specific CT dosimetry in routine clinical practice. The developed workflow integrates a robust open-source segmentation tool with organ/tissue-specific dose prediction neural networks.

Calibration and Validation of Simulation Parameters for Maize Straw Based on Discrete Element Method and Genetic Algorithm-Backpropagation.

There is a significant difference between the simulation effect and the actual effect in the design process of maize straw-breaking equipment due to the lack of accurate simulation model parameters in the breaking and processing of maize straw. This article used a combination of physical experiments, virtual simulation, and machine learning to calibrate the simulation parameters of maize straw. A bimodal-distribution discrete element model of maize straw was established based on the intrinsic and contact parameters measured via physical experiments. The significance analysis of the simulation parameters was conducted via the Plackett-Burman experiment. The Poisson ratio, shear modulus, and normal stiffness of the maize straw significantly impacted the peak compression force of the maize straw and steel plate. The steepest-climb test was carried out for the significance parameter, and the relative error between the peak compression force in the simulation test and the peak compression force in the physical test was used as the evaluation index. It was found that the optimal range intervals for the Poisson ratio, shear modulus, and normal stiffness of the maize straw were 0.32-0.36, 1.24 × 108-1.72 × 108 Pa, and 5.9 × 106-6.7 × 106 N/m3, respectively. Using the experimental data of the central composite design as the dataset, a GA-BP neural network prediction model for the peak compression force of maize straw was established, analyzed, and evaluated. The GA-BP prediction model's accuracy was verified via experiments. It was found that the ideal combination of parameters was a Poisson ratio of 0.357, a shear modulus of 1.511 × 108 Pa, and a normal stiffness of 6.285 × 106 N/m3 for the maize straw. The results provide a basis for analyzing the damage mechanism of maize straw during the grinding process.